Package 'Foreign'
Total Page:16
File Type:pdf, Size:1020Kb
Load more
										Recommended publications
									
								- 
												
												Introduction to IDL®
Introduction to IDL® Revised for Print March, 2016 ©2016 Exelis Visual Information Solutions, Inc., a subsidiary of Harris Corporation. All rights reserved. ENVI and IDL are registered trademarks of Harris Corporation. All other marks are the property of their respective owners. This document is not subject to the controls of the International Traffic in Arms Regulations (ITAR) or the Export Administration Regulations (EAR). Contents 1 Introduction To IDL 5 1.1 Introduction . .5 1.1.1 What is ENVI? . .5 1.1.2 ENVI + IDL, ENVI, and IDL . .6 1.1.3 ENVI Resources . .6 1.1.4 Contacting Harris Geospatial Solutions . .6 1.1.5 Tutorials . .6 1.1.6 Training . .7 1.1.7 ENVI Support . .7 1.1.8 Contacting Technical Support . .7 1.1.9 Website . .7 1.1.10 IDL Newsgroup . .7 2 About This Course 9 2.1 Manual Organization . .9 2.1.1 Programming Style . .9 2.2 The Course Files . 11 2.2.1 Installing the Course Files . 11 2.3 Starting IDL . 11 2.3.1 Windows . 11 2.3.2 Max OS X . 11 2.3.3 Linux . 12 3 A Tour of IDL 13 3.1 Overview . 13 3.2 Scalars and Arrays . 13 3.3 Reading Data from Files . 15 3.4 Line Plots . 15 3.5 Surface Plots . 17 3.6 Contour Plots . 18 3.7 Displaying Images . 19 3.8 Exercises . 21 3.9 References . 21 4 IDL Basics 23 4.1 IDL Directory Structure . 23 4.2 The IDL Workbench . 24 4.3 Exploring the IDL Workbench . - 
												
												A Comparative Evaluation of Matlab, Octave, R, and Julia on Maya 1 Introduction
A Comparative Evaluation of Matlab, Octave, R, and Julia on Maya Sai K. Popuri and Matthias K. Gobbert* Department of Mathematics and Statistics, University of Maryland, Baltimore County *Corresponding author: [email protected], www.umbc.edu/~gobbert Technical Report HPCF{2017{3, hpcf.umbc.edu > Publications Abstract Matlab is the most popular commercial package for numerical computations in mathematics, statistics, the sciences, engineering, and other fields. Octave is a freely available software used for numerical computing. R is a popular open source freely available software often used for statistical analysis and computing. Julia is a recent open source freely available high-level programming language with a sophisticated com- piler for high-performance numerical and statistical computing. They are all available to download on the Linux, Windows, and Mac OS X operating systems. We investigate whether the three freely available software are viable alternatives to Matlab for uses in research and teaching. We compare the results on part of the equipment of the cluster maya in the UMBC High Performance Computing Facility. The equipment has 72 nodes, each with two Intel E5-2650v2 Ivy Bridge (2.6 GHz, 20 MB cache) proces- sors with 8 cores per CPU, for a total of 16 cores per node. All nodes have 64 GB of main memory and are connected by a quad-data rate InfiniBand interconnect. The tests focused on usability lead us to conclude that Octave is the most compatible with Matlab, since it uses the same syntax and has the native capability of running m-files. R was hampered by somewhat different syntax or function names and some missing functions. - 
												
												Sage 9.4 Reference Manual: Finite Rings Release 9.4
Sage 9.4 Reference Manual: Finite Rings Release 9.4 The Sage Development Team Aug 24, 2021 CONTENTS 1 Finite Rings 1 1.1 Ring Z=nZ of integers modulo n ....................................1 1.2 Elements of Z=nZ ............................................ 15 2 Finite Fields 39 2.1 Finite Fields............................................... 39 2.2 Base Classes for Finite Fields...................................... 47 2.3 Base class for finite field elements.................................... 61 2.4 Homset for Finite Fields......................................... 69 2.5 Finite field morphisms.......................................... 71 3 Prime Fields 77 3.1 Finite Prime Fields............................................ 77 3.2 Finite field morphisms for prime fields................................. 79 4 Finite Fields Using Pari 81 4.1 Finite fields implemented via PARI’s FFELT type............................ 81 4.2 Finite field elements implemented via PARI’s FFELT type....................... 83 5 Finite Fields Using Givaro 89 5.1 Givaro Finite Field............................................ 89 5.2 Givaro Field Elements.......................................... 94 5.3 Finite field morphisms using Givaro................................... 102 6 Finite Fields of Characteristic 2 Using NTL 105 6.1 Finite Fields of Characteristic 2..................................... 105 6.2 Finite Fields of characteristic 2...................................... 107 7 Miscellaneous 113 7.1 Finite residue fields........................................... - 
												
												A Fast Dynamic Language for Technical Computing
Julia A Fast Dynamic Language for Technical Computing Created by: Jeff Bezanson, Stefan Karpinski, Viral B. Shah & Alan Edelman A Fractured Community Technical work gets done in many different languages ‣ C, C++, R, Matlab, Python, Java, Perl, Fortran, ... Different optimal choices for different tasks ‣ statistics ➞ R ‣ linear algebra ➞ Matlab ‣ string processing ➞ Perl ‣ general programming ➞ Python, Java ‣ performance, control ➞ C, C++, Fortran Larger projects commonly use a mixture of 2, 3, 4, ... One Language We are not trying to replace any of these ‣ C, C++, R, Matlab, Python, Java, Perl, Fortran, ... What we are trying to do: ‣ allow developing complete technical projects in a single language without sacrificing productivity or performance This does not mean not using components in other languages! ‣ Julia uses C, C++ and Fortran libraries extensively “Because We Are Greedy.” “We want a language that’s open source, with a liberal license. We want the speed of C with the dynamism of Ruby. We want a language that’s homoiconic, with true macros like Lisp, but with obvious, familiar mathematical notation like Matlab. We want something as usable for general programming as Python, as easy for statistics as R, as natural for string processing as Perl, as powerful for linear algebra as Matlab, as good at gluing programs together as the shell. Something that is dirt simple to learn, yet keeps the most serious hackers happy.” Collapsing Dichotomies Many of these are just a matter of design and focus ‣ stats vs. linear algebra vs. strings vs. - 
												
												Statistics and GIS Assistance Help with Statistics
Statistics and GIS assistance An arrangement for help and advice with regard to statistics and GIS is now in operation, principally for Master’s students. How do you seek advice? 1. The users, i.e. students at INA, make direct contact with the person whom they think can help and arrange a time for consultation. Remember to be well prepared! 2. Doctoral students and postdocs register the time used in Agresso (if you have questions about this contact Gunnar Jensen). Help with statistics Research scientist Even Bergseng Discipline: Forest economy, forest policies, forest models Statistical expertise: Regression analysis, models with random and fixed effects, controlled/truncated data, some time series modelling, parametric and non-parametric effectiveness analyses Software: Stata, Excel Postdoc. Ole Martin Bollandsås Discipline: Forest production, forest inventory Statistics expertise: Regression analysis, sampling Software: SAS, R Associate Professor Sjur Baardsen Discipline: Econometric analysis of markets in the forest sector Statistical expertise: General, although somewhat “rusty”, expertise in many econometric topics (all-rounder) Software: Shazam, Frontier Associate Professor Terje Gobakken Discipline: GIS og long-term predictions Statistical expertise: Regression analysis, ANOVA and PLS regression Software: SAS, R Ph.D. Student Espen Halvorsen Discipline: Forest economy, forest management planning Statistical expertise: OLS, GLS, hypothesis testing, autocorrelation, ANOVA, categorical data, GLM, ANOVA Software: (partly) Shazam, Minitab og JMP Ph.D. Student Jan Vidar Haukeland Discipline: Nature based tourism Statistical expertise: Regression and factor analysis Software: SPSS Associate Professor Olav Høibø Discipline: Wood technology Statistical expertise: Planning of experiments, regression analysis (linear and non-linear), ANOVA, random and non-random effects, categorical data, multivariate analysis Software: R, JMP, Unscrambler, some SAS Ph.D. - 
												
												Introduction to Ggplot2
Introduction to ggplot2 Dawn Koffman Office of Population Research Princeton University January 2014 1 Part 1: Concepts and Terminology 2 R Package: ggplot2 Used to produce statistical graphics, author = Hadley Wickham "attempt to take the good things about base and lattice graphics and improve on them with a strong, underlying model " based on The Grammar of Graphics by Leland Wilkinson, 2005 "... describes the meaning of what we do when we construct statistical graphics ... More than a taxonomy ... Computational system based on the underlying mathematics of representing statistical functions of data." - does not limit developer to a set of pre-specified graphics adds some concepts to grammar which allow it to work well with R 3 qplot() ggplot2 provides two ways to produce plot objects: qplot() # quick plot – not covered in this workshop uses some concepts of The Grammar of Graphics, but doesn’t provide full capability and designed to be very similar to plot() and simple to use may make it easy to produce basic graphs but may delay understanding philosophy of ggplot2 ggplot() # grammar of graphics plot – focus of this workshop provides fuller implementation of The Grammar of Graphics may have steeper learning curve but allows much more flexibility when building graphs 4 Grammar Defines Components of Graphics data: in ggplot2, data must be stored as an R data frame coordinate system: describes 2-D space that data is projected onto - for example, Cartesian coordinates, polar coordinates, map projections, ... geoms: describe type of geometric objects that represent data - for example, points, lines, polygons, ... aesthetics: describe visual characteristics that represent data - for example, position, size, color, shape, transparency, fill scales: for each aesthetic, describe how visual characteristic is converted to display values - for example, log scales, color scales, size scales, shape scales, .. - 
												
												01 Introduction Lab.Pdf
AN INTRODUCTION TO R DEEPAYAN SARKAR Introduction and examples What is R?. R provides an environment in which you can perform statistical analysis and produce graphics. It is actually a complete programming language, although that is only marginally described in this book. |Peter Dalgaard, \Introductory Statistics with R", 2002 R can be viewed as a programming language that happens to come with a large library of pre-defined functions that can be used to perform various tasks. A major focus of these pre-defined functions is statistical data analysis, and these allow R to be used purely as a toolbox for standard statistical techniques. However, some knowledge of R programming is essential to use it well at any level. For advanced users in particular, the main appeal of R (as opposed to other data analysis software) is as a programming environment suited to data analysis. Our goal will be to learn R as a statistics toolbox, but with a fairly strong emphasis on its programming language aspects. In this tutorial, we will do some elementary statistics, learn to use the documentation system, and learn about common data structures and programming features in R. Some follow-up tutorials are available for self-study at http://www.isid.ac.in/~deepayan/R-tutorials/. For more resources, see the R Project homepage http://www.r-project.org, which links to various manuals and other user-contributed documentation. A list of books related to R is available at http://www.r-project.org/doc/bib/R-jabref.html. An excellent introductory book is Peter Dalgaard, \Introductory Statistics with R", Springer (2002). - 
												
												Regression Models by Gretl and R Statistical Packages for Data Analysis in Marine Geology Polina Lemenkova
Regression Models by Gretl and R Statistical Packages for Data Analysis in Marine Geology Polina Lemenkova To cite this version: Polina Lemenkova. Regression Models by Gretl and R Statistical Packages for Data Analysis in Marine Geology. International Journal of Environmental Trends (IJENT), 2019, 3 (1), pp.39 - 59. hal-02163671 HAL Id: hal-02163671 https://hal.archives-ouvertes.fr/hal-02163671 Submitted on 3 Jul 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Distributed under a Creative Commons Attribution| 4.0 International License International Journal of Environmental Trends (IJENT) 2019: 3 (1),39-59 ISSN: 2602-4160 Research Article REGRESSION MODELS BY GRETL AND R STATISTICAL PACKAGES FOR DATA ANALYSIS IN MARINE GEOLOGY Polina Lemenkova 1* 1 ORCID ID number: 0000-0002-5759-1089. Ocean University of China, College of Marine Geo-sciences. 238 Songling Rd., 266100, Qingdao, Shandong, P. R. C. Tel.: +86-1768-554-1605. Abstract Received 3 May 2018 Gretl and R statistical libraries enables to perform data analysis using various algorithms, modules and functions. The case study of this research consists in geospatial analysis of Accepted the Mariana Trench, a hadal trench located in the Pacific Ocean. - 
												
												Statistics with Free and Open-Source Software
Free and Open-Source Software • the four essential freedoms according to the FSF: • to run the program as you wish, for any purpose • to study how the program works, and change it so it does Statistics with Free and your computing as you wish Open-Source Software • to redistribute copies so you can help your neighbor • to distribute copies of your modified versions to others • access to the source code is a precondition for this Wolfgang Viechtbauer • think of ‘free’ as in ‘free speech’, not as in ‘free beer’ Maastricht University http://www.wvbauer.com • maybe the better term is: ‘libre’ 1 2 General Purpose Statistical Software Popularity of Statistical Software • proprietary (the big ones): SPSS, SAS/JMP, • difficult to define/measure (job ads, articles, Stata, Statistica, Minitab, MATLAB, Excel, … books, blogs/posts, surveys, forum activity, …) • FOSS (a selection): R, Python (NumPy/SciPy, • maybe the most comprehensive comparison: statsmodels, pandas, …), PSPP, SOFA, Octave, http://r4stats.com/articles/popularity/ LibreOffice Calc, Julia, … • for programming languages in general: TIOBE Index, PYPL, GitHut, Language Popularity Index, RedMonk Rankings, IEEE Spectrum, … • note that users of certain software may be are heavily biased in their opinion 3 4 5 6 1 7 8 What is R? History of S and R • R is a system for data manipulation, statistical • … it began May 5, 1976 at: and numerical analysis, and graphical display • simply put: a statistical programming language • freely available under the GNU General Public License (GPL) → open-source - 
												
												Comparative Analysis of Statistic Software Used in Education of Non- Statisticians Students
Recent Advances in Computer Engineering, Communications and Information Technology Comparative analysis of statistic software used in education of non- statisticians students KLARA RYBENSKA; JOSEF SEDIVY, LUCIE KUDOVA Department of Technical subjects, Departement of Informatics Faculty of Education, Fakulty of Science, Faculty of Arts University of Hradec Kralove Rokitanskeho 62, 500 03 Hradec Kralove CZECH REPUBLIC [email protected] http://www.uhk.cz [email protected] http://www.uhk.cz, [email protected] http://www.uhk.cz Abstract: - Frequently used tool for processing of statistical data in the field of science and humanities IBM SPSS program. This is a very powerful tool, which is an unwritten standard. Its main disadvantage is the high price, which is restrictive for use in an academic environment, not only in teaching but also in the case of individual student work on their own computers. Currently, there are two tools that could at least partially IBM SPSS for teaching science disciplines to replace. These are programs PSPP (http://www.gnu.org/software/pspp/) and alternative (SOFA http://www.sofastatistics.com). Both are available under a license that permits their free use not only for learning but also for commercial purposes. This article aims to find out which are the most common ways of using IBM SPSS program at the University of Hradec Králové and suggest a possible alternative to the commercial program to use in teaching non-statistical data processing student study programs. Key-Words: - statistic software, open source, IBM SPSS, PSPP, data processing, science education. 1 Introduction using formal symbolic system. They then certainly Quantitative research uses logical reasoning process one of the methods of mathematical , statistical and Very simply, you can lay out this process in a few other, but such methods of abstract algebra , formal steps: Getting Started formulation of the research logic, probability theory, which are not restricted to problem, which should be read plenty of literature, numerical data nature. - 
												
												The Use of PSPP Software in Learning Statistics. European Journal of Educational Research, 8(4), 1127-1136
Research Article doi: 10.12973/eu-jer.8.4.1127 European Journal of Educational Research Volume 8, Issue 4, 1127 - 1136. ISSN: 2165-8714 http://www.eu-jer.com/ The Use of PSPP Software in Learning Statistics Minerva Sto.-Tomas Darin Jan Tindowen* Marie Jean Mendezabal University of Saint Louis, PHILIPPINES University of Saint Louis, PHILIPPINES University of Saint Louis, PHILIPPINES Pyrene Quilang Erovita Teresita Agustin University of Saint Louis, PHILIPPINES University of Saint Louis, PHILIPPINES Received: July 8, 2019 ▪ Revised: August 31, 2019 ▪ Accepted: October 4, 2019 Abstract: This descriptive and correlational study investigated the effects of using PSPP in learning Statistics on students’ attitudes and performance. The respondents of the study were 200 Grade 11 Senior High School students who were enrolled in Probability and Statistics subject during the Second Semester of School Year 2018-2019. The respondents were randomly selected from those classes across the different academic strands that used PSPP in their Probability and Statistics subject through stratified random sampling. The results revealed that the students have favorable attitudes towards learning Statistics with the use of the PSPP software. The students became more interested and engaged in their learning of statistics which resulted to an improved academic performance. Keywords: Probability and statistics, attitude, PSPP software, academic performance, technology. To cite this article: Sto,-Tomas, M., Tindowen, D. J, Mendezabal, M. J., Quilang, P. & Agustin, E. T. (2019). The use of PSPP software in learning statistics. European Journal of Educational Research, 8(4), 1127-1136. http://doi.org/10.12973/eu-jer.8.4.1127 Introduction The rapid development of technology has brought remarkable changes in the modern society and in all aspects of life such as in politics, trade and commerce, and education. - 
												
												Insight MFR By
Manufacturers, Publishers and Suppliers by Product Category 11/6/2017 10/100 Hubs & Switches ASCEND COMMUNICATIONS CIS SECURE COMPUTING INC DIGIUM GEAR HEAD 1 TRIPPLITE ASUS Cisco Press D‐LINK SYSTEMS GEFEN 1VISION SOFTWARE ATEN TECHNOLOGY CISCO SYSTEMS DUALCOMM TECHNOLOGY, INC. GEIST 3COM ATLAS SOUND CLEAR CUBE DYCONN GEOVISION INC. 4XEM CORP. ATLONA CLEARSOUNDS DYNEX PRODUCTS GIGAFAST 8E6 TECHNOLOGIES ATTO TECHNOLOGY CNET TECHNOLOGY EATON GIGAMON SYSTEMS LLC AAXEON TECHNOLOGIES LLC. AUDIOCODES, INC. CODE GREEN NETWORKS E‐CORPORATEGIFTS.COM, INC. GLOBAL MARKETING ACCELL AUDIOVOX CODI INC EDGECORE GOLDENRAM ACCELLION AVAYA COMMAND COMMUNICATIONS EDITSHARE LLC GREAT BAY SOFTWARE INC. ACER AMERICA AVENVIEW CORP COMMUNICATION DEVICES INC. EMC GRIFFIN TECHNOLOGY ACTI CORPORATION AVOCENT COMNET ENDACE USA H3C Technology ADAPTEC AVOCENT‐EMERSON COMPELLENT ENGENIUS HALL RESEARCH ADC KENTROX AVTECH CORPORATION COMPREHENSIVE CABLE ENTERASYS NETWORKS HAVIS SHIELD ADC TELECOMMUNICATIONS AXIOM MEMORY COMPU‐CALL, INC EPIPHAN SYSTEMS HAWKING TECHNOLOGY ADDERTECHNOLOGY AXIS COMMUNICATIONS COMPUTER LAB EQUINOX SYSTEMS HERITAGE TRAVELWARE ADD‐ON COMPUTER PERIPHERALS AZIO CORPORATION COMPUTERLINKS ETHERNET DIRECT HEWLETT PACKARD ENTERPRISE ADDON STORE B & B ELECTRONICS COMTROL ETHERWAN HIKVISION DIGITAL TECHNOLOGY CO. LT ADESSO BELDEN CONNECTGEAR EVANS CONSOLES HITACHI ADTRAN BELKIN COMPONENTS CONNECTPRO EVGA.COM HITACHI DATA SYSTEMS ADVANTECH AUTOMATION CORP. BIDUL & CO CONSTANT TECHNOLOGIES INC Exablaze HOO TOO INC AEROHIVE NETWORKS BLACK BOX COOL GEAR EXACQ TECHNOLOGIES INC HP AJA VIDEO SYSTEMS BLACKMAGIC DESIGN USA CP TECHNOLOGIES EXFO INC HP INC ALCATEL BLADE NETWORK TECHNOLOGIES CPS EXTREME NETWORKS HUAWEI ALCATEL LUCENT BLONDER TONGUE LABORATORIES CREATIVE LABS EXTRON HUAWEI SYMANTEC TECHNOLOGIES ALLIED TELESIS BLUE COAT SYSTEMS CRESTRON ELECTRONICS F5 NETWORKS IBM ALLOY COMPUTER PRODUCTS LLC BOSCH SECURITY CTC UNION TECHNOLOGIES CO FELLOWES ICOMTECH INC ALTINEX, INC.